Generating Table and Column Statistics

Use the COMPUTE STATS statement to collect table and column statistics. The COMPUTE STATS variants offer different tradeoffs between computation cost, staleness, and maintenance workflows.

COMPUTE STATS

The COMPUTE STATS command collects and sets the table-level and partition-level row counts as well as all column statistics for a given table. The collection process is CPU-intensive and can take a long time to complete for very large tables.

To speed up COMPUTE STATS consider the following options which can be combined.
  • Limit the number of columns for which statistics are collected to increase the efficiency of COMPUTE STATS. Queries benefit from statistics for those columns involved in filters, join conditions, group by or partition by clauses. Other columns are good candidates to exclude from COMPUTE STATS. This feature is available since Impala 2.12.

  • Set the MT_DOP query option to use more threads within each participating impalad to compute the statistics faster - but not more efficiently. Note that computing stats on a large table with a high MT_DOP value can negatively affect other queries running at the same time if the COMPUTE STATS claims most CPU cycles.

  • Consider the experimental extrapolation and sampling features (see below) to further increase the efficiency of computing stats.

COMPUTE STATS is intended to be run periodically, e.g. weekly, or on-demand when the contents of a table have changed significantly. Due to the high resource utilization and long response time of tCOMPUTE STATS, it is most practical to run it in a scheduled maintenance window where the Impala cluster is idle enough to accommodate the expensive operation. The degree of change that qualifies as “significant” depends on the query workload, but typically, if 30% of the rows have changed then it is recommended to recompute statistics.

If you reload a complete new set of data for a table, but the number of rows and number of distinct values for each column is relatively unchanged from before, you do not need to recompute stats for the table.

Extrapolation and Sampling

Impala supports extrapolation and sampling to alleviate the following common issues for computing and maintaining statistics on very large tables:
  • Newly added partitions do not have row count statistics. Table scans that only access those new partitions are treated as not having stats. Similarly, table scans that access both new and old partitions estimate the scan cardinality based on those old partitions that have stats, and the new partitions without stats are treated as having 0 rows.

  • The row counts of existing partitions become stale when data is added or dropped.

  • Computing stats for tables with a 100,000 or more partitions might fail or be very slow due to the high cost of updating the partition metadata in the Hive Metastore.

  • With transient compute resources it is important to minimize the time from starting a new cluster to successfully running queries. Since the cluster might be relatively short-lived, users might prefer to quickly collect stats that are "good enough" as opposed to spending a lot of time and resources on computing full-fidelity stats.

For very large tables, it is often wasteful or impractical to run a full COMPUTE STATS to address the scenarios above on a frequent basis.

The sampling feature makes COMPUTE STATS more efficient by processing a fraction of the table data, and the extrapolation feature aims to reduce the frequency at which COMPUTE STATS needs to be re-run by estimating the row count of new and modified partitions.

The sampling and extrapolation features are disabled by default. They can be enabled globally or for specific tables, as follows.

  • Set the impalad start-up configuration --enable_stats_extrapolation to enable the features globally.
  • To enable them only for a specific table, set the impala.enable.stats.extrapolation table property to true for the table. The table-level property overrides the global setting, so it is also possible to enable sampling and extrapolation globally, but disable it for specific tables by setting the table property to false. For example:
    ALTER TABLE mytable test_table SET TBLPROPERTIES("impala.enable.stats.extrapolation"="true");
Stats Extrapolation

The main idea of stats extrapolation is to estimate the row count of new and modified partitions based on the result of the last COMPUTE STATS. Enabling stats extrapolation changes the behavior of COMPUTE STATS, as well as the cardinality estimation of table scans. COMPUTE STATS no longer computes and stores per-partition row counts, and instead, only computes a table-level row count together with the total number of file bytes in the table at that time. No partition metadata is modified. The input cardinality of a table scan is estimated by converting the data volume of relevant partitions to a row count, based on the table-level row count and file bytes statistics. It is assumed that within the same table, different sets of files with the same data volume correspond to the similar number of rows on average. With extrapolation enabled, the scan cardinality estimation ignores per-partition row counts. It only relies on the table-level statistics and the scanned data volume.

The SHOW TABLE STATS and EXPLAIN commands distinguish between row counts stored in the Hive Metastore, and the row counts extrapolated based on the above process.

Sampling
A TABLESAMPLE clause may be added to COMPUTE STATS to limit the percentage of data to be processed. The final statistics are obtained by extrapolating the statistics from the data sample over the entire table. The extrapolated statistics are stored in the Hive Metastore, just as if no sampling was used. The following example runs COMPUTE STATS over a 10 percent data sample.
COMPUTE STATS test_table TABLESAMPLE SYSTEM(10) ;

We have found that a 10 percent sampling rate typically offers a good tradeoff between statistics accuracy and execution cost. A sampling rate well below 10 percent has shown poor results and is not recommended.

COMPUTE INCREMENTAL STATS

In Impala 2.1.0 and higher, you can use the COMPUTE INCREMENTAL STATS and DROP INCREMENTAL STATS commands. The INCREMENTAL clauses work with incremental statistics, a specialized feature for partitioned tables.

When you compute incremental statistics for a partitioned table, by default Impala only processes those partitions that do not yet have incremental statistics. By processing only newly added partitions, you can keep statistics up to date without incurring the overhead of reprocessing the entire table each time.

You can also compute or drop statistics for a specified subset of partitions by including a PARTITION clause in the COMPUTE INCREMENTAL STATS or DROP INCREMENTAL STATS statement.

The metadata for incremental statistics is handled differently from the original style of statistics:

  • Issuing a COMPUTE INCREMENTAL STATS without a partition clause causes Impala to compute incremental stats for all partitions that do not already have incremental stats. This might be the entire table when running the command for the first time, but subsequent runs should only update new partitions. You can force updating a partition that already has incremental stats by issuing a DROP INCREMENTAL STATS before running COMPUTE INCREMENTAL STATS.

  • The SHOW TABLE STATS and SHOW PARTITIONS statements now include an additional column showing whether incremental statistics are available for each column. A partition could already be covered by the original type of statistics based on a prior COMPUTE STATS statement, as indicated by a value other than -1 under the #Rows column. Impala query planning uses either kind of statistics when available.

  • COMPUTE INCREMENTAL STATS takes more time than COMPUTE STATS for the same volume of data. Therefore it is most suitable for tables with large data volume where new partitions are added frequently, making it impractical to run a full COMPUTE STATS operation for each new partition. For unpartitioned tables, or partitioned tables that are loaded once and not updated with new partitions, use the original COMPUTE STATS syntax.

  • COMPUTE INCREMENTAL STATS uses some memory in the catalogd process, proportional to the number of partitions and number of columns in the applicable table. The memory overhead is approximately 400 bytes for each column in each partition. This memory is reserved in the catalogd daemon, the statestored daemon, and in each instance of the impalad daemon.

  • In cases where new files are added to an existing partition, issue a REFRESH statement for the table, followed by a DROP INCREMENTAL STATS and COMPUTE INCREMENTAL STATS sequence for the changed partition.

  • The DROP INCREMENTAL STATS statement operates only on a single partition at a time. To remove statistics (whether incremental or not) from all partitions of a table, issue a DROP STATS statement with no INCREMENTAL or PARTITION clauses.

The following considerations apply to incremental statistics when the structure of an existing table is changed (known as schema evolution):

  • If you use an ALTER TABLE statement to drop a column, the existing statistics remain valid and COMPUTE INCREMENTAL STATS does not rescan any partitions.

  • If you use an ALTER TABLE statement to add a column, Impala rescans all partitions and fills in the appropriate column-level values the next time you run COMPUTE INCREMENTAL STATS.

  • If you use an ALTER TABLE statement to change the data type of a column, Impala rescans all partitions and fills in the appropriate column-level values the next time you run COMPUTE INCREMENTAL STATS.

  • If you use an ALTER TABLE statement to change the file format of a table, the existing statistics remain valid and a subsequent COMPUTE INCREMENTAL STATS does not rescan any partitions.